1 Effect of UPSTM-Based Decorrelation on Feature Discovery

1.0.1 Loading the libraries

library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(TH.data)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)

1.1 Material and Methods

About Dataset Coronaviruses are a broad family of viruses that have been linked to illnesses ranging from the common cold to more serious conditions such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS) (SARS. In 2019, a new coronavirus (COVID-19) was discovered in Wuhan, China.

Sometimes, When PCR test resources are scarce and antigen test kits are inaccurate, clinicians look for alternate COVID-19 testing methods that can be completed in a day and handle thousands of samples. COVID-19 virus proteins should be lacking in normal people’s saliva. Some distinct proteins may be produced in response to COVID-19 infection and can be utilized as a signature to identify potentially infected people. Therefore, protein profiles in a patient’s saliva can indicate that he or she is infected with COVID-19.

Mass spectrometry is a method for determining the protein composition of a material. Saliva samples from hundreds of patients were studied in this dataset. So, a machine learning specialist is approached and charged with developing a machine learning model that can identify who COVID-19 infected since the PCR test cannot interpret the result completely.

1.2 The Data

https://www.kaggle.com/datasets/kerneler/saliva-testing-dataset?select=COVID-19_MS_dataset_train.csv


COVID_19_MS <- read.csv("~/GitHub/LatentBiomarkers/Data/COVID19_MS/COVID-19_MS_dataset_train.csv")

colnames(COVID_19_MS) <- str_replace_all(colnames(COVID_19_MS),"\\.","_")
COVID_19_MS$Person_ID <- NULL
sampleID <- unique(COVID_19_MS$Sample_ID)
spectraID <- colnames(COVID_19_MS)[str_detect(colnames(COVID_19_MS),"X")]

avgCOVID19 <- NULL
class <- COVID_19_MS[!duplicated(COVID_19_MS$Sample_ID),"PCR_result"]
for (id in sampleID)
{
  avgCOVID19 <- rbind(avgCOVID19,apply(COVID_19_MS[COVID_19_MS$Sample_ID %in% id,spectraID],2,mean))
}

avgCOVID19 <- as.data.frame(avgCOVID19)
rownames(avgCOVID19) <- sampleID
avgCOVID19$class <- 1*(class=="pos")

pander::pander(table(avgCOVID19$class))

0	1
37	109

1.2.0.1 Standarize the names for the reporting

studyName <- "COVID19_MS"
dataframe <- avgCOVID19
outcome <- "class"
thro <- 0.80
TopVariables <- 10
cexheat = 0.25

1.3 Generaring the report

1.3.1 Libraries

Some libraries

library(psych)
library(whitening)
library("vioplot")
library("rpart")

1.3.2 Data specs

pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))

rows	col
146	2715

pander::pander(table(dataframe[,outcome]))

0	1
37	109


varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]

largeSet <- length(varlist) > 1500

1.3.3 Scaling the data

Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns


  ### Some global cleaning
  sdiszero <- apply(dataframe,2,sd) > 1.0e-16
  dataframe <- dataframe[,sdiszero]

  varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
  tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
  dataframe <- dataframe[,tokeep]

  varlist <- colnames(dataframe)
  varlist <- varlist[varlist != outcome]
  
  iscontinous <- sapply(apply(dataframe,2,unique),length) > 5 ## Only variables with enough samples



dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData

1.4 The heatmap of the data

numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000


if (!largeSet)
{

  hm <- heatMaps(data=dataframeScaled[1:numsub,],
                 Outcome=outcome,
                 Scale=TRUE,
                 hCluster = "row",
                 xlab="Feature",
                 ylab="Sample",
                 srtCol=45,
                 srtRow=45,
                 cexCol=cexheat,
                 cexRow=cexheat
                 )
  par(op)
}

1.4.0.1 Correlation Matrix of the Data

The heat map of the data


if (!largeSet)
{

  par(cex=0.6,cex.main=0.85,cex.axis=0.7)
  #cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
  cormat <- cor(dataframe[,varlist],method="pearson")
  cormat[is.na(cormat)] <- 0
  gplots::heatmap.2(abs(cormat),
                    trace = "none",
  #                  scale = "row",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Original Correlation",
                    cexRow = cexheat,
                    cexCol = cexheat,
                     srtCol=45,
                     srtRow=45,
                    key.title=NA,
                    key.xlab="|Pearson Correlation|",
                    xlab="Feature", ylab="Feature")
  diag(cormat) <- 0
  print(max(abs(cormat)))
}

1.5 The decorrelation


DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#> 
#>  Included: 2328 , Uni p: 0.004316151 , Uncorrelated Base: 1297 , Outcome-Driven Size: 0 , Base Size: 1297 
#> 
#> 
 1 <R=0.994,r=0.972,N=   27>, Top: 11( 1 )[ 1 : 11 Fa= 11 : 0.972 ]( 11 , 14 , 0 ),<|>Tot Used: 25 , Added: 14 , Zero Std: 0 , Max Cor: 0.970
#> 
 2 <R=0.970,r=0.960,N=   27>, Top: 5( 1 )[ 1 : 5 Fa= 14 : 0.960 ]( 4 , 5 , 11 ),<|>Tot Used: 33 , Added: 5 , Zero Std: 0 , Max Cor: 0.959
#> 
 3 <R=0.959,r=0.955,N=   27>, Top: 3( 3 )[ 1 : 3 Fa= 15 : 0.955 ]( 3 , 6 , 14 ),<|>Tot Used: 37 , Added: 6 , Zero Std: 0 , Max Cor: 0.954
#> 
 4 <R=0.954,r=0.952,N=   27>, Top: 1( 1 )[ 1 : 1 Fa= 15 : 0.952 ]( 1 , 1 , 15 ),<|>Tot Used: 38 , Added: 1 , Zero Std: 0 , Max Cor: 0.951
#> 
 5 <R=0.951,r=0.925,N=   31>, Top: 14( 1 )[ 1 : 14 Fa= 23 : 0.925 ]( 14 , 16 , 15 ),<|>Tot Used: 59 , Added: 16 , Zero Std: 0 , Max Cor: 0.925
#> 
 6 <R=0.925,r=0.913,N=   31>, Top: 15( 1 )[ 1 : 15 Fa= 33 : 0.913 ]( 15 , 17 , 23 ),<|>Tot Used: 85 , Added: 17 , Zero Std: 0 , Max Cor: 0.912
#> 
 7 <R=0.912,r=0.906,N=   31>, Top: 6( 2 )[ 1 : 6 Fa= 36 : 0.906 ]( 6 , 8 , 33 ),<|>Tot Used: 96 , Added: 8 , Zero Std: 0 , Max Cor: 0.905
#> 
 8 <R=0.905,r=0.903,N=   31>, Top: 3( 1 )[ 1 : 3 Fa= 38 : 0.903 ]( 3 , 3 , 36 ),<|>Tot Used: 101 , Added: 3 , Zero Std: 0 , Max Cor: 0.902
#> 
 9 <R=0.902,r=0.851,N=  105>, Top: 38( 1 )[ 1 : 38 Fa= 65 : 0.851 ]( 38 , 57 , 38 ),<|>Tot Used: 175 , Added: 57 , Zero Std: 0 , Max Cor: 0.885
#> 
 10 <R=0.885,r=0.843,N=  105>, Top: 8( 1 )[ 1 : 8 Fa= 70 : 0.843 ]( 8 , 9 , 65 ),<|>Tot Used: 188 , Added: 9 , Zero Std: 0 , Max Cor: 0.842
#> 
 11 <R=0.842,r=0.800,N=  145>, Top: 53( 1 )[ 1 : 53 Fa= 102 : 0.800 ]( 53 , 72 , 70 ),<|>Tot Used: 283 , Added: 72 , Zero Std: 0 , Max Cor: 0.871
#> 
 12 <R=0.871,r=0.800,N=  145>, Top: 3( 1 )[ 1 : 3 Fa= 105 : 0.800 ]( 3 , 4 , 102 ),<|>Tot Used: 289 , Added: 4 , Zero Std: 0 , Max Cor: 0.800
#> 
 13 <R=0.800,r=0.800,N=    0>
#> 
 [ 13 ], 0.7981248 Decor Dimension: 289 Nused: 289 . Cor to Base: 148 , ABase: 30 , Outcome Base: 0 
#> 
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]

pander::pander(sum(apply(dataframe[,varlist],2,var)))

23332

pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))

3247

pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))

0.132

pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))

0.324

1.5.1 The decorrelation matrix


if (!largeSet)
{

  par(cex=0.6,cex.main=0.85,cex.axis=0.7)
  
  UPSTM <- attr(DEdataframe,"UPSTM")
  
  gplots::heatmap.2(1.0*(abs(UPSTM)>0),
                    trace = "none",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Decorrelation matrix",
                    cexRow = cexheat,
                    cexCol = cexheat,
                   srtCol=45,
                   srtRow=45,
                    key.title=NA,
                    key.xlab="|Beta|>0",
                    xlab="Output Feature", ylab="Input Feature")
  
  par(op)
}

1.6 The heatmap of the decorrelated data

if (!largeSet)
{

  hm <- heatMaps(data=DEdataframe[1:numsub,],
                 Outcome=outcome,
                 Scale=TRUE,
                 hCluster = "row",
                 cexRow = cexheat,
                 cexCol = cexheat,
                 srtCol=45,
                 srtRow=45,
                 xlab="Feature",
                 ylab="Sample")
  par(op)
}

1.7 The correlation matrix after decorrelation

if (!largeSet)
{

  cormat <- cor(DEdataframe[,varlistc],method="pearson")
  cormat[is.na(cormat)] <- 0
  
  gplots::heatmap.2(abs(cormat),
                    trace = "none",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "Correlation after IDeA",
                    cexRow = cexheat,
                    cexCol = cexheat,
                     srtCol=45,
                     srtRow=45,
                    key.title=NA,
                    key.xlab="|Pearson Correlation|",
                    xlab="Feature", ylab="Feature")
  
  par(op)
  diag(cormat) <- 0
  print(max(abs(cormat)))
}

1.8 U-MAP Visualization of features

1.8.1 The UMAP based on LASSO on Raw Data


if (nrow(dataframe) < 1000)
{
  classes <- unique(dataframe[1:numsub,outcome])
  raincolors <- rainbow(length(classes))
  names(raincolors) <- classes
  datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
  plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
  text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}

1.8.2 The decorralted UMAP

if (nrow(dataframe) < 1000)
{

  datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
  plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
  text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}

1.9 Univariate Analysis

1.9.1 Univariate



univarRAW <- uniRankVar(varlist,
               paste(outcome,"~1"),
               outcome,
               dataframe,
               rankingTest="AUC")

100 : X297_73 200 : X382_77 300 : X479_57 400 : X629_81 500 : X796_11
600 : X969_8 700 : X1173_74 800 : X1364_32 900 : X1599_26 1000 : X1848_42
1100 : X2201_1 1200 : X2498_63 1300 : X2860_34 1400 : X3257_2 1500 : X3669_6
1600 : X4208_08 1700 : X4836_61 1800 : X5379_65 1900 : X6237_78 2000 : X7115_25
2100 : X8025_19 2200 : X9106_13 2300 : X10504_79 2400 : X11887_21 2500 : X13358_42
2600 : X14760_46




univarDe <- uniRankVar(varlistc,
               paste(outcome,"~1"),
               outcome,
               DEdataframe,
               rankingTest="AUC",
               )

100 : X297_73 200 : X382_77 300 : X479_57 400 : La_X629_81 500 : X796_11
600 : La_X969_8 700 : X1173_74 800 : X1364_32 900 : X1599_26 1000 : X1848_42
1100 : X2201_1 1200 : X2498_63 1300 : X2860_34 1400 : X3257_2 1500 : X3669_6
1600 : X4208_08 1700 : X4836_61 1800 : X5379_65 1900 : La_X6237_78 2000 : X7115_25
2100 : X8025_19 2200 : X9106_13 2300 : X10504_79 2400 : X11887_21 2500 : X13358_42
2600 : X14760_46

1.9.2 Final Table


univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")

##topfive
topvar <- c(1:length(varlist)) <= TopVariables
pander::pander(univarRAW$orderframe[topvar,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	ROCAUC
X301_02	1.0971	1.288	5.780	4.428	0.37901	0.901
X1714_44	0.9653	1.354	2.775	2.143	0.16574	0.876
X1752_66	0.4318	0.300	1.092	0.570	0.52591	0.868
X1539_08	0.4536	0.386	1.205	0.790	0.08336	0.866
X900_35	0.7958	0.696	2.402	2.230	0.03660	0.841
X440_87	1.8551	1.815	4.788	3.289	0.14184	0.839
X1522_71	0.5368	0.438	1.262	0.787	0.54259	0.836
X3325_02	39.9174	33.638	5.890	11.627	0.00130	0.833
X3428_53	0.0818	0.313	0.566	1.047	0.00325	0.829
X256_5	2.4015	2.773	8.994	9.942	0.00576	0.824



topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]

theLaVar <- rownames(finalTable)[str_detect(rownames(finalTable),"La_")]

pander::pander(univarDe$orderframe[topLAvar,univariate_columns])

	caseMean	caseStd	controlMean	controlStd	controlKSP	ROCAUC
X301_02	1.0971	1.2879	5.7798	4.4284	0.37901	0.901
X1714_44	0.9653	1.3543	2.7750	2.1432	0.16574	0.876
X1752_66	0.4318	0.3003	1.0915	0.5697	0.52591	0.868
X1539_08	0.4536	0.3864	1.2050	0.7901	0.08336	0.866
X900_35	0.7958	0.6958	2.4018	2.2300	0.03660	0.841
X440_87	1.8551	1.8155	4.7878	3.2888	0.14184	0.839
X1522_71	0.5368	0.4383	1.2620	0.7869	0.54259	0.836
X3428_53	0.0818	0.3132	0.5656	1.0472	0.00325	0.829
X256_5	2.4015	2.7730	8.9943	9.9421	0.00576	0.824
X2870_03	0.6581	0.8780	1.5602	1.0686	0.75629	0.821
La_X7409_15	-0.0162	0.0816	-0.0638	0.0568	0.56572	0.757
La_X2962_99	0.2415	0.2010	0.0423	0.4892	0.05789	0.733
La_X3325_02	4.0996	5.4444	1.3749	2.1974	0.12652	0.723
La_X2472_01	0.3016	0.3318	0.7323	0.5305	0.47880	0.723
La_X2671_39	0.5193	0.6291	0.8706	0.8453	0.02388	0.722


dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")

theSigDc <- dc[theLaVar]
names(theSigDc) <- NULL
theSigDc <- unique(names(unlist(theSigDc)))


theFormulas <- dc[rownames(finalTable)]
deFromula <- character(length(theFormulas))
names(deFromula) <- rownames(finalTable)

pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))

mean	total	fraction
2.01	211	0.0795



allSigvars <- names(dc)



dx <- names(deFromula)[1]
for (dx in names(deFromula))
{
  coef <- theFormulas[[dx]]
  cname <- names(theFormulas[[dx]])
  names(cname) <- cname
  for (cf in names(coef))
  {
    if (cf != dx)
    {
      if (coef[cf]>0)
      {
        deFromula[dx] <- paste(deFromula[dx],
                               sprintf("+ %5.3f*%s",coef[cf],cname[cf]))
      }
      else
      {
        deFromula[dx] <- paste(deFromula[dx],
                               sprintf("%5.3f*%s",coef[cf],cname[cf]))
      }
    }
  }
}

finalTable <- rbind(finalTable,univarRAW$orderframe[theSigDc[!(theSigDc %in% rownames(finalTable))],univariate_columns])


orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- deFromula[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]

Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")

finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])

	DecorFormula	caseMean	caseStd	controlMean	controlStd	controlKSP	ROCAUC	RAWAUC	fscores
X301_02		1.09708	1.2879	5.7798	4.4284	3.79e-01	0.901	0.901	NA
X1714_44		0.96530	1.3543	2.7750	2.1432	1.66e-01	0.876	0.876	NA
X1752_66		0.43183	0.3003	1.0915	0.5697	5.26e-01	0.868	0.868	NA
X1539_08		0.45356	0.3864	1.2050	0.7901	8.34e-02	0.866	0.866	NA
X900_35		0.79584	0.6958	2.4018	2.2300	3.66e-02	0.841	0.841	NA
X440_87		1.85509	1.8155	4.7878	3.2888	1.42e-01	0.839	0.839	1
X1522_71		0.53682	0.4383	1.2620	0.7869	5.43e-01	0.836	0.836	NA
X3325_02	NA	39.91739	33.6384	5.8901	11.6274	1.30e-03	0.833	0.833	NA
X3428_53		0.08181	0.3132	0.5656	1.0472	3.25e-03	0.829	0.829	NA
X256_5		2.40151	2.7730	8.9943	9.9421	5.76e-03	0.824	0.824	4
X2870_03		0.65813	0.8780	1.5602	1.0686	7.56e-01	0.821	0.821	NA
X3283_6	NA	7.93877	7.5390	1.0008	2.1724	6.86e-04	0.812	0.812	5
X3679_57	NA	0.16483	0.4102	0.3623	0.3880	4.48e-03	0.801	0.801	2
La_X7409_15	-0.293X3679_57 + 1.000X7409_15	-0.01621	0.0816	-0.0638	0.0568	5.66e-01	0.757	0.537	-1
La_X2962_99	+ 1.000X2962_99 -0.811X5722_3	0.24148	0.2010	0.0423	0.4892	5.79e-02	0.733	0.404	0
La_X3325_02	-4.512X3283_6 + 1.000X3325_02	4.09958	5.4444	1.3749	2.1974	1.27e-01	0.723	0.833	-1
La_X2472_01	-6.670X606_79 + 1.000X2472_01	0.30158	0.3318	0.7323	0.5305	4.79e-01	0.723	0.696	-1
La_X2671_39	-6.958X877_66 + 1.000X2671_39	0.51927	0.6291	0.8706	0.8453	2.39e-02	0.722	0.699	-1
X2671_39	NA	0.58852	0.5979	1.2166	2.2963	6.50e-05	0.699	0.699	NA
X2472_01	NA	0.40507	0.2742	0.9290	1.2921	1.05e-02	0.696	0.696	NA
X5722_3	NA	0.06519	0.0785	0.5365	1.1121	1.31e-03	0.662	0.662	3
X7409_15	NA	0.03202	0.1558	0.0422	0.1033	2.84e-05	0.537	0.537	NA
X877_66	NA	0.00995	0.0335	0.0497	0.3025	9.77e-10	0.460	0.460	2
X606_79	NA	0.01552	0.0425	0.0295	0.1575	4.02e-09	0.448	0.448	2
X2962_99	NA	0.29435	0.1837	0.4774	1.0962	1.23e-05	0.404	0.404	NA

1.10 Comparing IDeA vs PCA vs EFA

1.10.1 PCA

featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,tol=0.002)   #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous]) 
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)

#pander::pander(pc$rotation)


PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])


  gplots::heatmap.2(abs(PCACor),
                    trace = "none",
  #                  scale = "row",
                    mar = c(5,5),
                    col=rev(heat.colors(5)),
                    main = "PCA Correlation",
                    cexRow = 0.5,
                    cexCol = 0.5,
                     srtCol=45,
                     srtRow= -45,
                    key.title=NA,
                    key.xlab="Pearson Correlation",
                    xlab="Feature", ylab="Feature")

1.10.2 EFA


EFAdataframe <- dataframeScaled

if (length(iscontinous) < 2000)
{
  topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
  if (topred < 2) topred <- 2
  
  uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE)  # EFA analysis
  predEFA <- predict(uls,dataframeScaled[,iscontinous])
  EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
  colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous]) 


  
  EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
  
  
    gplots::heatmap.2(abs(EFACor),
                      trace = "none",
    #                  scale = "row",
                      mar = c(5,5),
                      col=rev(heat.colors(5)),
                      main = "EFA Correlation",
                      cexRow = 0.5,
                      cexCol = 0.5,
                       srtCol=45,
                       srtRow= -45,
                      key.title=NA,
                      key.xlab="Pearson Correlation",
                      xlab="Feature", ylab="Feature")
}

1.11 Effect on CAR modeling

par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")

  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(rawmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
  }


pander::pander(table(dataframe[,outcome],pr))

	0	1
0	31	6
1	4	105

pander::pander(ptab)

detail:

statistic	est	lower	upper
ap	0.7603	0.6827	0.8270
tp	0.7466	0.6680	0.8149
se	0.9633	0.9087	0.9899
sp	0.8378	0.6799	0.9381
diag.ac	0.9315	0.8776	0.9667
diag.or	135.6250	35.9750	511.3030
nndx	1.2482	1.0776	1.6990
youden	0.8011	0.5886	0.9280
pv.pos	0.9459	0.8861	0.9799
pv.neg	0.8857	0.7326	0.9680
lr.pos	5.9404	2.8532	12.3678
lr.neg	0.0438	0.0166	0.1158
p.rout	0.2397	0.1730	0.3173
p.rin	0.7603	0.6827	0.8270
p.tpdn	0.1622	0.0619	0.3201
p.tndp	0.0367	0.0101	0.0913
p.dntp	0.0541	0.0201	0.1139
p.dptn	0.1143	0.0320	0.2674

tab:

Outcome + Outcome - Total

Test + 105 6 111

Test - 4 31 35

Total 109 37 146
method: exact
digits: 2
conf.level: 0.95

	Outcome +	Outcome -	Total
Test +	105	6	111
Test -	4	31	35
Total	109	37	146

pander::pander(ptab$detail[c(5,3,4,6),])

	statistic	est	lower	upper
5	diag.ac	0.932	0.878	0.967
3	se	0.963	0.909	0.990
4	sp	0.838	0.680	0.938
6	diag.or	135.625	35.975	511.303


par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")

  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(IDeAmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
  }

pander::pander(table(DEdataframe[,outcome],pr))

	0	1
0	31	6
1	3	106

pander::pander(ptab)

detail:

statistic	est	lower	upper
ap	0.7671	0.69007	0.8330
tp	0.7466	0.66800	0.8149
se	0.9725	0.92167	0.9943
sp	0.8378	0.67986	0.9381
diag.ac	0.9384	0.88621	0.9714
diag.or	182.5556	43.14327	772.4619
nndx	1.2341	1.07254	1.6624
youden	0.8103	0.60153	0.9324
pv.pos	0.9464	0.88704	0.9801
pv.neg	0.9118	0.76322	0.9814
lr.pos	5.9969	2.88107	12.4826
lr.neg	0.0328	0.01067	0.1012
p.rout	0.2329	0.16698	0.3099
p.rin	0.7671	0.69007	0.8330
p.tpdn	0.1622	0.06193	0.3201
p.tndp	0.0275	0.00571	0.0783
p.dntp	0.0536	0.01991	0.1130
p.dptn	0.0882	0.01858	0.2368

tab:

Outcome + Outcome - Total

Test + 106 6 112

Test - 3 31 34

Total 109 37 146
method: exact
digits: 2
conf.level: 0.95

	Outcome +	Outcome -	Total
Test +	106	6	112
Test -	3	31	34
Total	109	37	146

pander::pander(ptab$detail[c(5,3,4,6),])

	statistic	est	lower	upper
5	diag.ac	0.938	0.886	0.971
3	se	0.972	0.922	0.994
4	sp	0.838	0.680	0.938
6	diag.or	182.556	43.143	772.462


par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
  plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
  text(PCAmodel, use.n = TRUE,cex=0.75)
  ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}

pander::pander(table(PCAdataframe[,outcome],pr))

	0	1
0	25	12
1	2	107

pander::pander(ptab)

detail:

statistic	est	lower	upper
ap	0.8151	0.74246	0.8744
tp	0.7466	0.66800	0.8149
se	0.9817	0.93529	0.9978
sp	0.6757	0.50215	0.8199
diag.ac	0.9041	0.84435	0.9466
diag.or	111.4583	23.44538	529.8682
nndx	1.5213	1.22304	2.2861
youden	0.6573	0.43743	0.8176
pv.pos	0.8992	0.83048	0.9468
pv.neg	0.9259	0.75710	0.9909
lr.pos	3.0268	1.89972	4.8224
lr.neg	0.0272	0.00676	0.1092
p.rout	0.1849	0.12555	0.2575
p.rin	0.8151	0.74246	0.8744
p.tpdn	0.3243	0.18014	0.4979
p.tndp	0.0183	0.00223	0.0647
p.dntp	0.1008	0.05320	0.1695
p.dptn	0.0741	0.00910	0.2429

tab:

Outcome + Outcome - Total

Test + 107 12 119

Test - 2 25 27

Total 109 37 146
method: exact
digits: 2
conf.level: 0.95

	Outcome +	Outcome -	Total
Test +	107	12	119
Test -	2	25	27
Total	109	37	146

pander::pander(ptab$detail[c(5,3,4,6),])

	statistic	est	lower	upper
5	diag.ac	0.904	0.844	0.947
3	se	0.982	0.935	0.998
4	sp	0.676	0.502	0.820
6	diag.or	111.458	23.445	529.868



par(op)

1.11.1 EFA


  EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
  EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
  pr <- predict(EFAmodel,EFAdataframe,type = "class")
  
  ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
  if (length(unique(pr))>1)
  {
    plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
    text(EFAmodel, use.n = TRUE,cex=0.75)
    ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
  }


  pander::pander(table(EFAdataframe[,outcome],pr))

	0	1
0	31	6
1	4	105

  pander::pander(ptab)

detail:

statistic	est	lower	upper
ap	0.7603	0.6827	0.8270
tp	0.7466	0.6680	0.8149
se	0.9633	0.9087	0.9899
sp	0.8378	0.6799	0.9381
diag.ac	0.9315	0.8776	0.9667
diag.or	135.6250	35.9750	511.3030
nndx	1.2482	1.0776	1.6990
youden	0.8011	0.5886	0.9280
pv.pos	0.9459	0.8861	0.9799
pv.neg	0.8857	0.7326	0.9680
lr.pos	5.9404	2.8532	12.3678
lr.neg	0.0438	0.0166	0.1158
p.rout	0.2397	0.1730	0.3173
p.rin	0.7603	0.6827	0.8270
p.tpdn	0.1622	0.0619	0.3201
p.tndp	0.0367	0.0101	0.0913
p.dntp	0.0541	0.0201	0.1139
p.dptn	0.1143	0.0320	0.2674

tab:

Outcome + Outcome - Total

Test + 105 6 111

Test - 4 31 35

Total 109 37 146
method: exact
digits: 2
conf.level: 0.95

	Outcome +	Outcome -	Total
Test +	105	6	111
Test -	4	31	35
Total	109	37	146

  pander::pander(ptab$detail[c(5,3,4,6),])

	statistic	est	lower	upper
5	diag.ac	0.932	0.878	0.967
3	se	0.963	0.909	0.990
4	sp	0.838	0.680	0.938
6	diag.or	135.625	35.975	511.303

  par(op)

Decorrelation-Based Feature Discovery: COVID_19

Jose Tamez

2023-09-12

1 Effect of UPSTM-Based Decorrelation on Feature Discovery

1.0.1 Loading the libraries

1.1 Material and Methods

1.2 The Data

1.2.0.1 Standarize the names for the reporting

1.3 Generaring the report

1.3.1 Libraries

1.3.2 Data specs

1.3.3 Scaling the data

1.4 The heatmap of the data

1.4.0.1 Correlation Matrix of the Data

1.5 The decorrelation

1.5.1 The decorrelation matrix

1.6 The heatmap of the decorrelated data

1.7 The correlation matrix after decorrelation

1.8 U-MAP Visualization of features

1.8.1 The UMAP based on LASSO on Raw Data

1.8.2 The decorralted UMAP

1.9 Univariate Analysis

1.9.1 Univariate

1.9.2 Final Table

1.10 Comparing IDeA vs PCA vs EFA

1.10.1 PCA

1.10.2 EFA

1.11 Effect on CAR modeling

1.11.1 EFA